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dc.contributor.authorSershen, Naidoo
dc.contributor.authorGómez, Daviel
dc.contributor.authorEscalante, Doris
dc.date.accessioned2021-01-25T07:24:36Z
dc.date.available2021-01-25T07:24:36Z
dc.date.issued2020
dc.identifier.citationSershen, N. et al. (2020). Assessing the effects of in vitro imposed water stress on pineapple growth in relation to biochemical stress indicators using polynomial regression analysis. Notulae Botanicae Horti Agrobotanici Cluj-Napoca ,48(1), 162-170en_US
dc.identifier.issn1842-4309
dc.identifier.uri10.15835/NBHA48111844
dc.identifier.urihttp://hdl.handle.net/10566/5732
dc.description.abstractKnowing the mechanisms that operate under water stress in commercial crops, particularly those that can affect productivity, such as phenolic or cell wall metabolism, is becoming increasingly important in a scenario of global climate change. However, our understanding of how to analyse statistically the relationships between these commonly used biochemical markers of water stress and growth in crops like pineapple, needs to be improved. In the present work, we have addressed the question of whether polynomial regression analysis can be used to describe the influence of selected plant metabolites (chlorophylls, carotenoids, phenolics and aldehydes) on shoot biomass, in response to a mannitol-induced water stress in temporary immersion bioreactors (TIBs). Polynomial regression analysis has been applied to investigate plant stress responses in many species but is very seldom used in in vitro screening studies. Here, the relationship between biochemical indicators (x; independent variable) and shoot growth (y; dependent variable) has been characterised, with y modelled as an nth degree polynomial in x.en_US
dc.language.isoenen_US
dc.publisherAcademicPresen_US
dc.subjectBiostatisticsen_US
dc.subjectDroughten_US
dc.subjectIn vitro osmotic stressen_US
dc.subjectTemporary immersion bioreactors (TIBs)en_US
dc.subjectPlant metabolitesen_US
dc.titleAssessing the effects of in vitro imposed water stress on pineapple growth in relation to biochemical stress indicators using polynomial regression analysisen_US
dc.typeArticleen_US


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